NBER WORKING PAPER SERIES THE IMPACT OF HEALTH INSURANCE EXPANSION ON PHYSICIAN TREATMENT CHOICE: MEDICARE PART D AND PHYSICIAN PRESCRIBING

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1 NBER WORKING PAPER SERIES THE IMPACT OF HEALTH INSURANCE EXPANSION ON PHYSICIAN TREATMENT CHOICE: MEDICARE PART D AND PHYSICIAN PRESCRIBING Tanyan Hu Sandra L. Decker Shn-Y Chou Workng Paper NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambrdge, MA November 2014 We thank Mary E. Dely and partcpants at the 2012 Eastern Economc Assocaton Annual Meetng, 2012 Amercan Socety of Health Economsts Conference, and semnars at Harvard Unversty, Unversty of Pennsylvana, the Johns Hopkns Unversty and the Natonal Center for Health Statstcs, CDC. The fndngs and conclusons n ths artcle are those of the authors and do not necessarly represent the vews of the Centers for Dsease Control and Preventon and the Natonal Bureau of Economc Research. NBER workng papers are crculated for dscusson and comment purposes. They have not been peerrevewed or been subject to the revew by the NBER Board of Drectors that accompanes offcal NBER publcatons by Tanyan Hu, Sandra L. Decker, and Shn-Y Chou. All rghts reserved. Short sectons of text, not to exceed two paragraphs, may be quoted wthout explct permsson provded that full credt, ncludng notce, s gven to the source.

2 The Impact of Health Insurance Expanson on Physcan Treatment Choce: Medcare Part D and Physcan Prescrbng Tanyan Hu, Sandra L. Decker, and Shn-Y Chou NBER Workng Paper No November 2014 JEL No. I13,I18,I31 ABSTRACT We test the effect of the ntroducton of Medcare Part D on physcan prescrbng behavor by usng data on physcan vsts from the Natonal Ambulatory Medcal Care Survey (NAMCS) and for patents aged We use a combned DD-RD specfcaton that s an mprovement over ether the dfference-n-dfference (DD) or regresson dscontnuty (RD) desgns. Comparng the dscrete jump n outcomes at age 65 before and after 2006, we fnd a 35% ncrease n the number of prescrpton drugs prescrbed or contnued per vst and a 55% ncrease n the number of generc drugs prescrbed or contnued, provdng evdence of physcan response to changes n patent outof-pocket costs. Tanyan Hu Unversty of Pennsylvana 423 Guardan Dr 1404 Blockley Hall Phladelpha,PA tanyanh@mal.med.upenn.edu Sandra L. Decker Dstngushed Consultant Natonal Center for Health Statstcs Centers for Dsease Control and Preventon 3311 Toledo Road (Room 6107) Hyattsvlle, MD esp4@cdc.gov Shn-Y Chou Department of Economcs College of Busness and Economcs Lehgh Unversty 621 Taylor Street Bethlehem, PA and NBER syc2@lehgh.edu

3 1. Introducton In the past 50 years, resources devoted to health care have outpaced resources devoted to many other sectors of the U.S. economy. 1 The expanson of the thrd-party payment system, whch results n hgher medcal care utlzaton and may stmulate the development and adopton of advanced treatment choces, has often been cted as an mportant drvng force behnd rsng health expendtures (Wesbrod 1991). Because of asymmetrc nformaton between physcans and patents, physcans serve not only as provders of health care servces to patents, but also as advsors to patents, thus takng on a crucal role n the nterplay between health nsurance, health care utlzaton, and treatment choces (Arrow 1963). Indeed, physcan agency s consdered to be a possble explanaton for rsng health expendtures assocated wth nsurance coverage (Feldsten 1970). 2 Thus, understandng the mpact of health nsurance on physcans treatment decsons has become crtcal for dentfyng the sources of health expendture growth and for evaluatng a host of health polces that am to mprove the effcency and effcacy of health care delvery. The relatonshp between a patent s nsurance status and a physcan s treatment decson can be analyzed usng a standard prncpal-agent framework (Arrow 1963) n whch both prncpals (patents) and agents (physcans) maxmze ther expected utlty. As agents, physcans typcally are assumed to maxmze the combnaton of ncome and non-fnancal factors, such as utlty stemmng from adherence to standards of practce and ethcs, as well as concern for patent welfare (Chalkley and Malcomson 1998; Ells and McGure 1986). When nsurance coverage becomes avalable, physcans may recommend more procedures and servces f they take patents out-of-pocket costs nto consderaton (at least partal agency). Although some lterature has shown that physcans consder and ncorporate patents health nsurance status nto ther clncal decson makng (Wyna et al. 2003; Meyers et al. 2006), other work (Federman 2004) does not fnd evdence of ths. In fact, there exsts lttle emprcal work quantfyng the effect of a patent s health nsurance status on physcans treatment decsons. 1 Natonal health expendture as a share of GDP grew from about 5% n 1960 to about 17% n (Source: Reports/NatonalHealthExpendData/NatonalHealthAccountsHstorcal.html) 2 Physcan agency s a collectve term referrng to the ssues related to physcans market power, behavors and ncentves (McGure 2000). 3

4 We therefore attempt to contrbute to the lterature by examnng the mpact of the adopton of Medcare Part D on physcans prescrbng behavor. The ntroducton of Medcare Part D provdes a good opportunty for studyng the mpact of a patent s health nsurance status on physcans clncal decsons. Frst, the prmary goal of Medcare Part D, adopted n January 2006, s to provde drug coverage for Medcare benefcares. 3 It does not come wth supply-sde reforms, such as a change n the payment system or pay-for-performance, that mght restrct or encourage a change n physcans treatment patterns. So, our estmates should not be confounded by supply-sde ncentves. Second, unlke n some Asan countres (Izuka 2007; Y.-M. Lu, Yang, and Hseh 2009), U.S. physcans only prescrbe; they do not dspense drugs. So, although there s evdence that physcans alter practce patterns n response to fnancal ncentves (Gruber and Owngs 1996; Yp 1998), snce U.S. physcans are not compensated (ether by an nsurance rembursement system or by pharmaceutcal manufacturers) on the number or type of drugs they prescrbe, there are no obvous pecunary ncentves attrbutable to Part D for physcans to change prescrbng patterns. Fnally, there are typcally a number of pharmaceutcal optons for each therapeutc condton. The dscrete nature of pharmaceutcal treatments makes t easer to measure ths aspect of physcan treatment compared to others, and makes t possble to measure t beyond a bnary decson. Ths paper makes several contrbutons to the lterature. Frst, unlke prevous studes that examne the effect of nsurance on the use of health care servces (Card, Dobkn, and Maestas 2009; Anderson, Dobkn, and Gross 2014), whch s a combned decson of physcan and patent, we specfcally study only physcans treatment decsons. Usng the Natonal Ambulatory Medcal Care Surveys (NAMCS), a unque natonally-representatve survey of health care use durng physcan offce vsts, we study whether physcans changed the quantty and type of drugs they prescrbe after the adopton of Part D. Second, the nsttutonal and contextual features of Part D and pharmaceutcal decsons allow us to delve more deeply nto examnng how physcans act as agents. Ideally, f 3 Part D has snce become the prmary source of drug coverage for Medcare benefcares, coverng more than half of all benefcares (57%) n It decreased the percentage of Medcare benefcares wth no source of prescrpton drug coverage from 27% n 2003 to 10% n 2007 (The Henry J. Kaser Famly Foundaton 2003; The Henry J. Kaser Famly Foundaton 2006; The Henry J. Kaser Famly Foundaton 2007a), and lowered the fracton of prescrpton drug costs that many Medcare benefcares had to pay out-of-pocket at the pont of servce. 4

5 physcans act as perfect agents (Pauly 1980), they should make prescrbng decsons consderng out-of-pocket costs to the patent, takng nto account the cost-sharng features or formulary of beneft desgn (e.g.. patents costs are lower f generc drugs are prescrbed) of the patent s health nsurance plan. We estmate how physcans change prescrbng patterns for those aged 65 and over before and after the adopton of Part D, compared to changes for those under age 65. In a separate dataset whch contans nformaton on the utlzaton of prescrpton drugs, we also estmate relatve changes n the pattern of use of prescrpton drugs for those aged 65 and over before and after Smlar magntudes of the two estmates could ndcate that observed changes n patents use of prescrpton drugs due to changes n cost-sharng may come more from physcan behavor than from patent behavor. Thrd, we analyze whether physcans mght be double agents : agents not only for patents but also for pharmaceutcal companes. Studes show that physcans may change ther prescrbng patterns n response to marketng efforts of pharmaceutcal companes (Lure et al. 1990; Engelberg, Parsons, and Tefft 2013; Sacks 2013). In the context of Part D mplementaton, pharmaceutcal companes may ncrease ther promotonal actvtes (e.g. detalng, free samples). Whle hstorcally t has been hard for pharmaceutcal companes to compensate physcans drectly for prescrbng a partcular drug (Stern and Trajtenberg 1998), physcans prescrbng decsons may be affected by advertsng levels for prescrpton drugs. However, snce advertsng efforts ntensfed only margnally after the adopton of Part D, we argue that our emprcal strategy descrbed below can dfference out the mpact from advertsng levels smply by comparng the sample before and after We also perform several addtonal tests of whether physcans appear to act partcularly n the nterest of pharmaceutcal companes, n terms of prescrbng newer drugs or more brand-name drugs. Fourth, we employ a combned dfference-n-dfferences (DD) and regresson dscontnuty (RD) estmaton strategy (DD-RD hereafter), an mprovement over ether DD or RD alone. 5 The man dea of the DD-RD specfcaton s to compare the outcome dscontnuty at age 65 before the adopton of Part D to the outcome dscontnuty at age 65 after ts adopton. 4 Pror work found that advertsng of drugs ncreased only slghtly after the ntroducton of Part D, mostly among drug classes wth less competton (defned by the number of advertsed drugs wthn the drug class pror to Part D mplementaton) or among domnant drugs (defned by hgher market share of advertsng before the mplementaton of Part D). (Lakdawalla, Sood, and Gu 2013). 5 We are aware of two workng papers that also use smlar emprcal strateges: Chay, Km, and Swamna (2010) and Gremb et al. (2011). 5

6 Usng restrcted NAMCS data allowed us to dentfy the exact age (n days) of a patent at the tme of a physcan offce vst. The DD-RD specfcaton relaxes the strngent assumptons underlyng the DD and RD methods. Compared to DD, DD-RD allows treatment and control groups to have dfferent trends n outcomes by ncludng controls for the runnng varable, patents' age at the tme of vst, as a flexble polynomal functon and nteractng wth the treatment varable (year 2006 or later). Addtonally, we can estmate the effect of Part D by comparng the effect of turnng 65 before and after 2006, snce the confoundng effect of Medcare elgblty for most ndvduals at age 65 that would plague RD estmaton can be dfferenced out, assumng that the effect of overall Medcare elgblty on the use of medcal care s the same before and after We perform an extensve lst of robustness checks n order to ensure that all assumptons of the DD-RD specfcaton are met n our sample. In addton, we use supplementary datasets to examne the mpact of Part D on patents utlzaton of and expendture on prescrpton drugs. Ffth, ths paper contrbutes to a growng lterature that evaluates the varous effects of Part D, an mportant natonal health program accountng for a substantal fracton of health care spendng. 6 Most exstng studes focus on the effect of Part D on the use and out-of-pocket cost of prescrpton drugs (Lchtenberg and Sun 2007; Yn et al. 2008; Bresacher et al. 2011; Ketcham and Smon 2008; Engelhardt and Gruber 2011; F. X. Lu et al. 2011; Kaestner and Khan 2012), the utlzaton of non-drug health care servces (Engelhardt and Gruber 2011; F. X. Lu et al. 2011; Kaestner and Khan 2012; Zhang et al. 2009; Kaestner, Long, and Alexander 2014), drug prces (M. Duggan and Morton 2010; M. G. Duggan and Morton 2011), and pharmaceutcal companes R&D spendng (Blume-Kohout and Sood 2013). The effect of Part D on physcan prescrbng behavors has receved almost no attenton n prevous work despte the fact that s a potental mechansm behnd any changes n prescrpton drug consumpton or the use of other health care servces. For ths reason, the extent to whch physcans talor ther prescrbng to nsurance coverage after Part D s a fundamental, polcy-relevant queston that merts thorough examnaton. 6 Part D has shfted prescrpton drug costs from Medcad or other prvate payers to Medcare, and has contrbuted to a net ncrease n federal spendng. The Medcare share of total natonal spendng on prescrpton drugs ncreased from 2% n 2005 to 22% n 2006, and the net federal cost of the Part D program s estmated to be $982 bllon for nne years between 2007 and 2016 (The Henry J. Kaser Famly Foundaton 2007). 6

7 Fnally, ths paper adds to a broader lterature on physcan ncentves. A number of emprcal studes have shown that physcans respond to a change n fnancal ncentves by changng ther practce of medcne, ncludng ther treatment choce (Gruber and Owngs 1996), hosptal referral patterns (Ho and Pakes 2013), or provson of electve procedures and adopton of technology (Clemens and Gottleb 2014). In the context of prescrpton drugs, prevous studes also show that physcans respond to markups of drugs n countres where prescrbng and dspensng drugs are not separate functons (Izuka 2012; Izuka 2007; Y.-M. Lu, Yang, and Hseh 2009; Lu 2014; Nguyen 2011). However, relatvely few emprcal studes have documented the response of physcans to non-pecunary ncentves, n part because of the dffculty n observng changes n non-pecunary ncentves. 7 The Part D settng ntroduces an arguably exogenous change that enables us to study the effect of non-fnancal ncentves on physcans behavor. 2. Background on Medcare Part D Pror to the passage of the Medcare Prescrpton Drug, Improvement, and Modernzaton Act of 2003 (MMA 2003), Medcare had only two fee-for-servce components, Part A and Part B, and one managed care component, Part C. Parts A and B are plans for npatent and outpatent care and related medcal servces, wth no comprehensve prescrpton drug coverage. Pror to Part D, some Medcare benefcares obtaned drug coverage through employer-sponsored plans, Medcare HMO plans, Medgap plans, Medcad plans, and state Pharmacy Assstance programs, though some sources of coverage had restrctve terms or hgh copayments. And n 2003, about 27% of senors aged 65 and above lacked any source of nsurance coverage for prescrpton drugs. Because of the lack of drug coverage, many benefcares were payng for prescrpton drugs out-of-pocket, and out-of-pocket costs for prescrpton drugs were ncreasng faster than for other types of health care servces. Medcare benefcares pad $644 out-of-pocket for prescrpton drugs n 2000 on average, rsng to $996 n 2003 (The Henry J. Kaser Famly Foundaton 2003). 7 Some past studes are closely related to the topc we study and have examned whether physcans respond to changes n patents' cost by adjustng ther prescrpton patterns when there s no fnancal ncentve for them to change behavor. Lundn (2000) found that physcans were less lkely to prescrbe trade-name versons of drugs to patents who have to pay a large sum out-of-pocket. There s also evdence that physcans ncrease prescrpton of a certan drug due to patent expraton (Carrera, Goldman, and Joyce 2013) or when the cost was covered by a thrd party (Dalen, Sorso, and Strom 2010). 7

8 In order to ensure access to prescrpton drugs and to lmt the fnancal burden assocated wth prescrpton drug costs, Medcare Part D was created as part of the MMA 2003, and went nto effect on January 1, Under Part D, elgble persons can partcpate voluntarly by enrollng n one of two types of prvate nsurance plans n ther area of resdence: a Prescrpton Drug Plan (PDP) that covers only prescrpton drugs, or a Medcare Advantage-Prescrpton Drug Plan (MA-PD) that covers both medcal servces and prescrpton drugs. 8 The adopton of Part D affected Medcare benefcares n several ways. Frst, t resulted n sharp changes n prescrpton drug coverage for those aged 65 and over after compared to before Although t s voluntary for Medcare benefcares to enroll n a Part D plan, about 53% of Medcare benefcares were enrolled n Part D plans n Senors had an ncentve to enroll f they had no plan pror to Part D or f they had a plan whose coverage was not as good as the coverage offered under Part D. (Benefcares could keep ther prevous plan as long as the coverage for prescrpton drugs was at least as comprehensve compared to a standard plan under Part D, n whch case the prevous plan s consdered credtable.) Only dual-elgble benefcares -- those Medcare benefcares who are also Medcad recpents -- are automatcally enrolled n Part D plans. After the ntal enrollment perod, the enrollment numbers contnued to grow, reachng around 60% n 2010 (The Henry J. Kaser Famly Foundaton 2010). The percentage of Medcare benefcares wthout any drug coverage decreased from 19% n 2002 to 10% n June To avod the problem of adverse selecton, Medcare benefcares were charged a fnancal penalty f they joned the program after May 15, 2006 unless they were able to demonstrate that they had access to credtable coverage elsewhere. Ths rule ensured that transton nto drug coverage plans for senors happened quckly n Part D substantally decreased the out-of-pocket (OOP) costs of prescrpton drugs for patents aged 65 and older relatve to patents under age 65 after 2006 compared to before 2006 (Lchtenberg and Sun 2007; Yn et al. 2008; Bresacher et al. 2011; Ketcham and Smon 2008; Engelhardt and Gruber 2011; F. X. Lu et al. 2011; Kaestner and Khan 2012). Medcare subsdzes the cost of plan coverage for all types of benefcares through varous types of plan subsdes, ncludng drect subsdes, ndvdual rensurance, rsk sharng payments, and low- 8 Benefcares enrolled n tradtonal Medcare (non-managed care) plan usually obtan drug coverage through a PDP; those enrolled n a managed care plan through a MA organzaton generally have to obtan drug coverage from MA-PD plans. In 2006, about 16.5 mllon people enrolled n stand-alone PDPs, 6 mllon people enrolled n MA- PD plans, and 6.8 mllon people had other forms of credtable coverage from employer or unons (The Henry J. Kaser Famly Foundaton 2006). 8

9 ncome subsdes. These subsdes target costs of prescrpton drugs at varous stages of plan coverage, or for specfc benefcares. For example, drect subsdes remburse plans for the cost durng the ntal coverage perod; ndvdual rensurance remburses plans for the cost durng perods of catastrophc coverage; and rsk-sharng payments fnance unexpectedly hgh costs to help wth plans potental losses. 9 The low ncome subsdy helps wth premum and prescrpton drug costs for those who are elgble for Medcad or those wth ncome under 150% of the federal poverty lne. 10 As a result, Medcare subsdzes about 75 percent of the cost of standard drug coverage on average. Last but not least, plans often set ther own formulary, whch s a tered structure wth dfferent levels of cost sharng across therapeutcally smlar drugs. Snce generc drugs are boequvalent to, but much less expensve than, ther brand name counterparts, prvate nsurers admnsterng Part D plans adopt varous formulary and beneft desgns to steer enrollees toward less-expensve generc alternatves. 11 Plans often place genercs on a ter of formulary wth the lowest copayment, preferred brand-name drugs are on a ter wth a hgher copayment, and nonpreferred brand name drugs are on a ter wth the hghest copayment. 12 Through desgn of plan formulares, Part D plans thus encourages the use of generc drugs, n order to help control costs n the plan and to provde a compettve enough premum to attract potental consumers. 3. Physcan Model In ths secton, we develop a model of physcan treatment decsons durng patent vsts, n a fashon smlar to the work n Acemoglu & Fnkelsten (2008). Our model allows us to study whether the expanson of drug coverage s theoretcally lkely to result n a change n physcan prescrbng behavor of the provson of other health care servces. A. Set Up of the Model 9 Drect subsdy s a monthly prospectve payment pad by CMS, whch manly remburses the plan for the cost of ntal coverage. Indvdual rensurance s provded for a certan percentage of drug spendng above enrollees catastrophc coverage. Medcare fnances some unexpectedly hgh costs, or acheves unexpectedly hgh profts n order to lmt plans potental losses or gans, whch are called rsk-sharng payments. 10 Low-ncome subsdes mean that those who are also elgble for Medcad or under 135% of the federal poverty lne pay no premum and have no deductble, and those whose ncome s between 135% and 150% of the federal poverty lne pay a reduced premum and annual deductble. Both groups have a lower copayment for covered drugs as a result. 11 Accordng to the Congressonal Budget Offce, generc drugs save consumers an estmated $8 to $10 bllon a year at retal pharmaces. 12 Part D plan formulares are subject to some regulaton. For example, they are requred to cover all drugs n a protected drug class and at least two drugs n each therapeutc class. 9

10 We model the physcan-patent relatonshp as a sngle perod nteracton. The patent vsts the doctor's offce, and the doctor uses dagnostc sklls and nformaton from varous sources about patents' nsurance coverage, as well as about drugs from pharmaceutcal companes, to make treatment decsons. Then the doctor wrtes a prescrpton. Assume the physcan cares about patents' utlty. The physcan s utlty functon durng ths one-tme nteracton can be wrtten as: D max w D, A, T f mf( D, A, T ) k d p D k p T C( A ),(1) where represents the number of drug treatments for patent, C A ) s the effort level physcans make to treat the patent, and d t t ( s the quantty of other medcal care servces performed n the physcan's offce for patent. 13 F ( ) s a producton functon, producng health from nputs of prescrpton drugs, other medcal care servces, and physcan effort. We assume that F ( ) s ncreasng n all three nputs and s twce contnuously dfferentable for postve levels of nput. Health enters postvely nto patents utlty functons and each unt of health s worth m. The lst prce of each unt of prescrpton drugs and other medcal care p d p t servces are and, respectvely. Although the generosty of coverage s dfferent, patents only need to pay a fracton of the lst prce for both prescrpton drugs and other medcal care servces. The fracton for prescrpton drugs s and for other servces s. In Eq. (1), physcans effort level s typcally not observable to patents. If physcans are perfect agents, they wll not only attend to ther patents health needs but also talor treatment plans to a patent s fnancal crcumstances. To acheve these goals, physcans wll need to acqure the necessary knowledge or nformaton about relevant nsurance polces, formulares, and drug pharmacology. The optmal level of physcan effort occurs when the margnal beneft of A s equal to zero. Nevertheless, when effort levels are hard to observe, physcans may nstead choose to act as perfect agents along a dmenson that s observable to patents. We wll dscuss physcan effort levels n more detal n Secton 3.C. T k d B. Physcan Choce About Drug Prescrbng and Other Treatment Decsons k t 13 In NAMCS, nformaton s also collected on whether some other tests are ordered or provded durng a physcan vst ncludng blood pressure check, X-ray, EKG/ECG, Pap test, urnalyss, PSA test, and CBC (complete blood count). 10

11 The adopton of Part D lowers the OOP cost of prescrpton drugs for Medcare patents. Specfcally, the out-of-pocket cost fracton of prescrpton drugs, analyze the effect of the adopton of Part D polcy as comparatve statcs of, decreases. Thus, we can. Gven the objectve functon Eq. (1), we have the followng propostons (proofs are shown n Appendx B): PROPOSITION 1: Let D (k d )and T (k d ) be the optmal choces for patent at the copay k d. D ( kd ) pd FDD Then 0. 2 k F F F d DD TT DT PROPOSITION 2: assume F D, A, T ) s homogeneous of degree 1n D and T, we get ( F( D, A, T ) G( A ) ( D, T ), n whch (, ) exhbts constant returns to scale. Let the local elastcty of substtuton between drug and non-drug treatment be DT T ( k k d d ) 0 f and only f 1 DT 1.. Thus we have Proposton 1 mples that when the copay for drug treatment decreases for a certan D ( kd ) patent, physcans ncrease the quantty of prescrpton drugs prescrbed, that s, 0. k However, the quantty of other health care provded could ncrease, decrease, or stay the same, dependng on the relatve sze of "decreasng returns" to scale ( ) and the elastcty of substtuton between prescrpton drugs and other servces ( DT DT ) (Proposton 2). For example, f 1,.e., the two types of treatments are complementary n the health producton functon, then the quantty of other servces wll ncrease as the copayment for prescrpton drugs decreases. If 1 and 1, then there s enough substtutablty between the two servces DT and the quantty of other servces wll decrease. C. Physcan Prescrpton of Generc Drugs To analyze the effect of Part D on physcans prescrpton of generc drugs, we separate prescrbed drugs n Eq. (1) nto two categores generc and branded drugs-- assumng physcans prescrbe D g unts of generc drugs and D b unts of branded drugs. The OOP cost fracton of the prce patents need to pay for generc drugs s k d g and for branded drugs s k d k d d k db. To 11

12 smplfy exposton wthout the loss of generalty, we leave T out of the equaton. Then the objectve functon can be wrtten as max w D g, D b f mf( D g, D b ) k dg p g D g k db p b D b, (2) PROPOSITION 3: Assumng F(D g,d b ) to be homothetc n D g and D b, then the move from less generous prescrpton drug coverage to more generous drug coverage may affect the generc- ' branded drug rato. Usng D g ' and D b to ndcate the number of prescrbed generc and branded drugs after the adopton of Part D, we have ' D g ' D b >=< D g f and only f k d g ' D b ' k db <=> k d g k db Proposton 3 mples that when the relatve generosty of nsurance coverage for generc and branded drugs changes, the rato of generc drugs and branded name drugs n physcans' prescrptons wll change as well. Specfcally, when Part D ncreases the generosty of coverage for generc drugs more than for branded drugs-- that s, the OOP cost fracton for generc drugs decreases more than that for branded drugs, k d g ' ' k db < k d g k db, the result wll be that the prescrpton of generc drugs ncreases more than that of branded drugs,.e.., ' D g ' D b > D g D b. (Otherwse, f the copayment was hgher for generc than branded drugs, then the prescrpton of generc drugs would ncrease less than that for branded drugs Dg Dg.) D. The Impact of Part D on Physcan (Non-Observable) Effort D b Prevously, we assumed that physcans efforts to acqure the necessary knowledge to make a treatment decson ncurred no cost. We relax ths assumpton here denotng the cost as, where s convex and monotonc. Together wth equaton (1), the optmal prescrpton decson and the optmal effort level (denoted as condtons: and. D b ) wll be characterzed by the frst order Suppose physcans do not act as perfect agents and maxmze ther own utlty,, where s patents utlty, denoted by equaton (1). We assume, ndcatng that physcans are not purely altrustc. Thus, to make a treatment decson, physcans maxmze the 12

13 objectve functon:. The optmal drug prescrpton D wll be characterzed by the same frst-order condton as equaton (1). 14 The optmal effort level (denoted as ) wll be characterzed by. The optmal effort level s lower than, because the margnal beneft s smaller. That s, when physcans do not act as perfect agents, effort s less and physcans may not respond to Part D by changng prescrpton behavor to better talor toward patents changed OOP costs. 4. Data Our man analyses use the Natonal Ambulatory Medcal Care Surveys (NAMCS) and to estmate the mpact of Medcare Part D on physcans' prescrbng patterns. The NAMCS s conducted by the Natonal Center for Health Statstcs. It collects data on a natonally-representatve sample of vsts to non-federally employed offce-based physcans n the U.S., excludng radologsts, anesthesologsts, and pathologsts. To select physcans for ntervew, NAMCS uses a multstage probablty sample desgn; ths desgn s descrbed elsewhere (Bryant and Shmzu 1988). Each physcan n the sample s randomly assgned to a one-week reportng perod, and nformaton on a systematc random sample of about 30 vsts s then collected. Physcans and patents may be selected multple tmes, but there s no dentfcaton number to lnk ether patents or physcans longtudnally. Each physcan s asked to record nformaton on up to eght drugs (sx drugs n 2002) that were ordered, suppled, admnstered, or contnued durng the sngle vst. Prescrpton drugs, over-the-counter drugs, mmunzatons, allergy shots, anesthetcs, chemotherapy, and detary supplements are ncluded. 16 We frst examne prescrbng patterns as measured by the number of drugs prescrbed or contnued durng a NAMCS vst. NAMCS has 27,973 vst records for patents aged years old for the sample perod and We lmt the sample to 26,474 vsts (9,737 for and 16,737 for ), excludng 1,509 (5.39%) vsts for patents who have mssng nformaton on any varable used n the analyses. 17 We also consder prescrbng patterns 14 The frst order condton for D s, where wll cancel out. 15 We exclude the year 2005 from the man analyss because of the possble exstence of antcpatory effects, whch we wll explan n detal n Secton 6.C.. 16 Informaton on whether drugs assocated wth a NAMCS vst were new or contnued was collected begnnng n 2005, when approxmately 70 percent of drugs were categorzed as contnued. 17 We mpute one varable, percent of revenue from Medcare patents, for 6.29 percent of the sample. Mssng values are mputed usng predcted values from a tobt regresson as a functon of year dummes, physcan specalty 13

14 as measured by the number of generc drugs prescrbed or contnued. We do ths by combnng nformaton on drugs prescrbed n the NAMCS wth nformaton from the Food and Drug Admnstraton (FDA) Orange Book database contanng generc status, as well as date of drug approval. 18 Although physcans may sometmes wrte down the brand name of a drug expectng that a generc equvalent may be substtuted at the pharmacy, we test whether physcans were more lkely to wrte down the generc rather than the brand name of drugs begnnng n 2006 for those aged 65 and over compared to our control group of ndvduals under age 65. (If the physcan wrtes down the brand name of a drug, the pharmacy may substtute the generc, though the reverse s not usually the case.) We also use prescrpton-level data to test whether any ncrease n prescrbng occurs partcularly for new drugs lkely to be the most heavly marketed by pharmaceutcal companes. For drugs wth dfferent manufacturers, strengths, or packages, we merge the earlest date of FDA approval for the actve ngredent contaned n the drug and applcaton type onto the NAMCS data. 19 We use the earlest date of approval for the actve ngredent snce we have no nformaton about drug formulaton n the NAMCS. Although some formulaton changes may brng about sgnfcant mprovements n drug delvery, others may be small changes such as a change from pll to lqud form. Snce prevous research fnds a strong overall correlaton between ntal drug approval (of an actve ngredent) and pharmaceutcal nvestment and advertsng (Congressonal Budget Offce (CBO) 2009; Vakratsas and Ambler 1999; Roth 1996), we therefore use the earlest date of approval. We use the drug name for mergng snce there are no other varables avalable that dentfy drugs n both the NAMCS data and the FDA data. We exclude over-the counter (OTC) drugs from our analyses usng drug nformaton from the NAMCS. After excludng both OTC and njectable drugs, the merge rate between the NAMCS and FDA data s 95%. (Most of the non-merged drugs were approved pror to 1982 snce the FDA orange book only contans approval dates for drugs approved snce 1982.) (The NAMCS data contan no nformaton on dosage.) Thus, we examne the effect of the ntroducton of dummes, nteractons between specalty dummes and the percent of populaton age 65 years or older, nteractons between specalty dummes and the percent of patents age 65 or older, whether the physcan use electronc medcal records. The cutoff value for vst level analyses 0.25 and for drug level analyses s The FDA orange book database contans nformaton on the date of FDA approval and applcaton type by drug name for drugs approved snce January 1, Applcaton type can be NDA or ANDA, whch ndcate whether t s a brand-name drug or generc drug, respectvely (avalable from 19 See Appendx A for a descrpton of the process of mergng FDA data wth the NAMCS data. 14

15 Medcare Part D on the overall number of drugs prescrbed or contnued, the number of generc drugs prescrbed or contnued, and the age of drugs prescrbed or contnued. For ths last varable, our prescrpton-level sample has 54,132 observatons for patents aged The NAMCS data also nclude nformaton on patents' demographc characterstcs, such as gender, race, and dagnoss categores defned by ICD-9 codes, and on physcan characterstcs ncludng metropoltan statstcal area (MSA) status, whether thers s a solo practce or not, ther adopton of electronc medcal records, specalty categores, and state of practce. We control for these varables as covarates n our specfcaton. Sample statstcs for our control varables are lsted n Table A2. Although our man dataset s the NAMCS, we also use the and Medcal Expendture Panel Survey (MEPS), a natonally-representatve survey of respondents drawn from the Natonal Health Intervew Survey, to examne the mpact of Part D on patents utlzaton of and expendture on prescrpton drugs. Although patents are requred to have a physcan s prescrpton before a drug can be dspensed, they typcally can decde whether or not to fll the prescrpton. To some extent, patents complance may reflect the extent to whch ther nterests are algned wth physcans objectve functons (equaton 1). Thus, f patents ncrease drug utlzaton after Part D by nearly as much as prescrptons ncrease, ths may suggest that physcan responses are algned wth patents preferences (as far as they can detect). MEPS s a two-year panel wth nformaton on the number of prescrptons flled, total expendture for prescrpton drugs, and expendture for drugs from dfferent sources, such as self-pay (OOP costs), Medcare, Medcad, or prvate sources. For ndependent varables, we control for patents gender, race/ethncty, poverty category, census regon, MSA status, educaton level, martal status, and pror health condtons. 20 Sample statstcs are reported n Table A3. After excludng observatons wth any mssng nformaton on the varables we use, we are left wth 276,774 observatons for the group of respondents aged Emprcal Strategy 20 Pror health condton ndcates whether the ndvdual has certan types of chronc condtons ncludng asthma, hgh blood pressure, angna, heart attack, jont pan, stroke, emphysema, arthrts or other heart dsease. 15

16 We propose an emprcal approach that combnes DD and RD (we call ths specfcaton DD-RD hereafter) n order to dentfy the average treatment effect of Medcare Part D, mplemented on January 1, 2006, for ndvduals aged 65 and over. The dfference-n-dfference (DD) approach s a commonly-used strategy to dentfy treatment effects. In the context of ths paper, we use t to estmate the effect of a certan polcy by explotng relatve changes n prescrpton drug utlzaton by patents aged 65 and older (.e., those aged 65-69, the treatment group) who vsted physcans offces before and after January 1, 2006, compared to those of patents under age 65 (.e., those aged 60-64, the control group) who vsted physcans offces before and after January 1, One weakness of the DD approach s that t assumes that trends n prescrbng patterns are the same for both the treatment and control groups pror to polcy mplementaton, so that the DD estmator dentfes only the effect of Part D. However, trends n prescrbng patterns are lkely to dffer by patent age, whch makes them dffer for treatment and control groups. 21 In some past lterature, the DD approach took ths dfference nto account by lmtng the sample to a narrow age range, but ths does not fully address the ssue or verfy the valdty of the basc assumptons of the DD model. The valdty of the assumpton of equal prescrpton trends for treatment and control groups pror to polcy mplementaton s even more questonable due to the possblty of antcpatory effects, as we wll dscuss n Secton 6.C. A second emprcal approach, more popular n recent polcy analyses, s the regresson dscontnuty (RD) desgn. Here, the man dea s to compare the change n outcomes for patents who are just before and just after age 65, usng only data from after polcy adopton (Card, Dobkn, and Maestas 2009). For the RD desgn, the key assumpton s that assgnment to ether sde of the dscontnuty threshold s as good as random (Lee and Lemeux 2010). Unfortunately, as mentoned earler, there s a confoundng polcy dscontnuty at the age 65 cutoff. Most ndvduals become elgble for Part D and for npatent and outpatent coverage at age 65 because they also become elgble for Medcare Parts A and B. If we adopted the RD specfcaton to analyze the effect of Part D, then t would volate the RD assumpton that potental outcomes are contnuous n the absence of treatment. Thus t would be dffcult to 21 We calculate the number of prescrpton and generc drugs prescrbed or contnued by physcans by age group and perform a Wald test to examne whether there s a statstcally sgnfcant dfference between means of outcomes between par-wse ages and p-values from results to reject the null hypothess that means are equal. 16

17 dsentangle the mpact of Part D polcy from the mpact of Medcare elgblty more generally, due to the confoundng effect of Medcare elgblty that occurs for most ndvduals at age 65. When we combne the two strateges and use the DD-RD specfcaton, we essentally estmate the dscontnutes n outcomes begnnng n 2006 for those aged 65 to 69 as compared to those aged 60 to 64. The estmaton equaton s as follows: Outcome 1( age 65) g( Age ) g( Days ) X 4 j ( year 2006) 2 Y 7 j State 8,(5) j 1( age 65) 1( year 2006) where Outcome j s the outcome (number of drugs prescrbed or contnued, the number of generc drugs prescrbed or contnued, number of brand name drugs prescrbed or contnued, etc.) for patent seen by physcan j; 1(age>=65) s an ndcator for whether patent s aged 65 or older, and the varable 1(year>=2006) ndcates whether the vst takes place on or after January 1, The specfcaton ncludes control varables for patent characterstcs, X (gender, race, year the vst took place, seasonal fxed effect, Charlson ndex 22, and dummes for the major dagnostc category assocated wth the vst 23 ), physcan practce characterstcs, Y j (specalty of the physcan 24, whether the physcan s n a solo practce or uses electronc medcal records, whether the practce s wthn a metropoltan statstcal area, and percentage of revenue from Medcare patents), and state fxed effects State j, whch control for any tmenvarant characterstcs of physcan practce patterns n dfferent states. We estmate the equaton usng ordnary least squares (OLS). 25 All analyses use sample weghts, and standard errors account for the complex desgn of the NAMCS survey usng Stata software verson 12 (Stata-Corp, College Staton, Texas). In order to best model physcan prescrbng patterns by patents age, we take advantage of the exact dates of brth n the NAMCS data. Accordngly, Age s the number of days between 22 The Charlson ndex s a numercal score ndcatng the severty of patent comorbdtes. Each condton s assocated wth a partcular score and the scores for each of three possble dagnoses assocated wth a NAMCS vst are totaled to calculate the ndex. The Charlson comorbdty ndex has been shown to predct subsequent mortalty (Charlson et al. 1987). 23 Major dsease category nclude nfectous and parastc dseases, neoplasms, endocrne, nutrtonal and metabolc dseases, and mmunty dsorders, mental dsorders, congental anomales, as well as dseases of the blood and blood-formng organs, nervous system and sense organs, crculatory system, respratory system, dgestve system, gentournary system, skn and subcutaneous tssue, musculoskeletal system and connectve tssue. Also symptoms, sgns, and ll-defned condtons. 24 Internal Medcne, Pedatrcs, General Surgery, Obstetrcs & Gynecology, Orthopedc Surgery, Cardovascular Dseases, Dermatology, Urology, Psychatry, Neurology, Ophthalmology, Otolaryngology, or other specaltes relatve to General/Famly Practce. 25 We also estmate the model usng Generalzed Least Square (GLM) regressons. The results are smlar to results we present from OLS regressons. 3 17

18 the patent's age at vst and hs/her 65th brthday. We nclude g(age ),whch s a flexble polynomal functon of Age fully nteracted wth age 65 and year 2006 dummes. By ncludng g(age ), we allow prescrbng patterns to take dfferent forms on ether sde of the age 65 cutoff, before and after year Our basc specfcaton uses a cubc form for age n the analyss of NAMCS data. 26 Days s the number of days between the vst date and December 31, 2001, the begnnng of our sample perod. By usng exact dates of vsts n the NAMCS data and ncludng g(days ), a flexble polynomal functon of Days and ther nteracton terms wth the year 2006 dummy, we can also more precsely model trends n physcan prescrbng patterns over tme, whle stll allowng for the dscrete change at the tme of adopton of the polcy. The dentfcaton strategy we present here requres less strngent assumptons than the RD or the DD specfcatons. We do not requre that the treatment and control groups experence the same trend n outcomes n the absence of polcy adopton, nor do we requre that all other patents and practce characterstcs be contnuous across the age 65 threshold. The only two assumptons requred by ths specfcaton are: The confoundng dscontnutes must be tme-nvarant. Ths s equvalent to the RD condton about contnuty of potental outcomes; and The treatment effect and the confoundng effect are addtve. Ths s equvalent to the addtvty condtons n the DD specfcaton (Gremb, Nanncn, and Troano 2011). Under the dentfyng assumpton that other determnants of prescrbng patterns are contnuous at the age 65 cutoff (defned by whether patents are at least at ther 65th brthday when they vst the physcan), both before and after year 2006, the coeffcent of the nteracton of the age 65 dummy and the year 2006 dummy (β 3 n Equaton (5)) wll be an unbased DD-RD estmate of the effect of Part D on outcomes. The DD-RD desgn enables us to dentfy the treatment effect whle addressng the nternal valdty ssue that was dscussed for the DD specfcaton. The ntuton s that the confoundng polcy, whch s Medcare program elgblty for most ndvduals at age 65, s tme-nvarant n the sample perod. We can estmate ths dscontnuty pror to the adopton of Part D, and subtract that effect from the estmated dscontnuty after the adopton of Part D. As 26 We decde the order of polynomal for the functon of age usng two methods. We compare the goodness-of-ft among specfcatons wth dfferent order of polynomal functon accordng to Lee and Lemeux (2010). We also adopt a Wald test to examne the jont sgnfcance of addtonal polynomal terms. The results for both tests are avalable upon request. 18

19 long as the dscontnuty resultng from the confoundng polcy s constant, we can address the confoundng ssue usng the DD-RD specfcaton. In addton to vst-level regressons, we perform prescrpton-level regressons to see whether the age of the actve ngredent contaned n drugs prescrbed or contnued by physcans changed begnnng n The dependent varable s AgeDrug jk (age of the actve ngredent contaned n the drug, measured n months between tme of vst to the physcans offce and date of FDA approval). We nclude all controls from Equaton (5); we also add fxed effects for prmary drug class categores. 27 We test the valdty of assumptons for the DD-RD specfcaton and the robustness of results n a number of ways. Frst, we check the densty of the runnng varable Age around the age 65 cutoff, both before and after year 2006, n order to test whether the elgblty crtera s manpulated around the threshold (McCrary 2008). Second, we estmate specfcatons usng dfferent age bandwdths and dfferent orders of polynomal functons of age, both wth and wthout covarates. Thrd, we mplement the DD-RD specfcaton wth covarates as outcomes n order to valdate the basc assumpton of the DD-RD specfcaton: that all underlyng characterstcs are smooth around the age 65 threshold n the absence of the treatment. Fourth, we mplement a placebo DD-RD estmaton at an arbtrary cutoff defned as age 64 and above. 6. The Impact of Part D on Physcans Prescrbng Patterns A. Graphcal Evdence We frst present graphcal evdence of the mpact of Part D on physcan prescrbng behavor, usng NAMCS data. Fgure 1 plots the mean for the number of prescrpton drugs prescrbed or contnued by physcans by age per quarter, as well as the ftted age profles from regressons that nclude cubc terms n age (n days from/to 65th brthday), and full nteractons wth age 65 and year 2006 dummes. There s a small but notceable decrease n the number of drugs prescrbed or contnued when patents turn 65 for the sample. (A statstcal test of the dfference n the jumps at age 65 for the relatve to the samples n Fgure 1 s contaned n the top panel of Table 1 (frst column, model wthout covarates) and dscussed below.) After the adopton of Part D, the average number of drugs prescrbed or 27 One ssue wth the data s that the FDA orange book database assgns the value of Pror to Jan 1, 1982'' to drugs approved before For ths reason, we use censored normal regresson for the prescrpton-level analyss wth the outcome of age of drugs prescrbed, allowng the censorng value to vary from observaton to observaton. 19

20 contnued per patent ncreases from 2.4 prescrptons for patents under 65 to around 2.7 prescrptons when patents turn 65. Fgure 2 shows the mean for the number of generc drugs that physcans prescrbe or contnue per quarter by patent age and the ftted age profles from regressons wth a cubc functon of age for each sde of the age 65 and the year 2006 cutoffs. There s no dscernble jump n the outcome from below age 65 to above age 65 before polcy adopton. The average number of generc drugs prescrbed or contnued durng patent vsts does ncrease from 0.9 prescrptons to more than 1.2 prescrptons, or roughly a 0.3 prescrpton ncrease, after polcy adopton. (A statstcal test of the dfference n the jumps at age 65 for the relatve to the samples n Fgure 2 s contaned n the top panel of Table 1 (sxth column, model wthout covarates) and dscussed below.) B. Regresson Results In the top panel of Table 1, we summarze the regresson estmates of the change n the number of all prescrpton drugs and generc drugs prescrbed or contnued. 28 The frst column s the DD-RD specfcaton wthout any covarates, allowng for a cubc functons of age on ether sde of the 65 and year 2006 cutoffs. The coeffcent on the nteracton term between the age 65 dummy and the year 2006 dummy s postve and sgnfcant. Ths ndcates that there s a 0.61 prescrpton ncrease for patents aged 65 and above relatve to patents under age 65 after adopton of the Part D polcy. Next we separate the and the samples and run a conventonal RD regresson wth each. The results are shown n the second and thrd columns of the top panel. For the regresson usng the sample after 2006, the coeffcent of nterest s postve and sgnfcant, mplyng that there s a 0.3 prescrpton ncrease for patents aged 65 and above. Ths ncrease s smlar n magntude to that of the dscontnuty from Fgure 1. For the sample, the RD coeffcent s negatve wth a magntude of 0.29 prescrptons. Ths mples that before the adopton of the Part D polcy, the number of drugs prescrbed or contnued at physcan vsts decreased dscontnuously at age 65. That negatve coeffcent may reflect the fact that patents aged 65 and above were lkely to lose ther prescrpton drug coverage from employer-sponsored nsurance or other publc/prvate coverage at age 65. Subtractng the estmated effect for the 28 We also performed GLM regresson wth a log lnk and Gaussan dstrbuton, as determned by the Park test. The sgnfcance of results from OLS regresson stll holds and the calculated margnal effects are smlar to what we have from OLS regressons. 20

21 sample before 2006 from the effect for the sample after 2006, we fnd a 0.6 prescrpton change for the group of patents aged 65 and above relatve to patents under age 65 after the adopton of Part D. In the fourth column, the estmated DD-RD coeffcent s slghtly lower after controllng for covarates than the estmate wthout controls, but s stll sgnfcant wth a 0.56 prescrpton ncrease. Ths supports the assumpton that there are no other changes occurrng at age 65 n 2006 that are confoundng our analyss. It also confrms our observaton from Fgure 1, that physcans ncrease ther prescrptons (ether new or contnued) to patents aged 65 and above by 35 percent on average (calculated as dvded by , the mean for the number of drugs prescrbed or contnued for non- patents aged 65 and above before 2006 from Table A1) after the adopton of Part D, compared to drugs prescrbed or contnued for patents under age 65. The coeffcent on the nteracton term s stll sgnfcant f we change the order of the polynomal functon of age to a quartc functon n column (5). Consstent results across dfferent specfcatons mply that the estmated effect s robust to varous functonal forms for age,.e., that the sgnfcance of estmates of the mpact of Part D s not an artfact of how we specfy the age control functon. Columns (6) - (10) summarze results for the number of generc drugs prescrbed or contnued. In the top panel, the estmated result for the regresson wth a cubc functon of age s postve and sgnfcant, ndcatng an ncrease of 0.26 prescrptons on average. Ths s smlar to the magntude of the ncrease shown n Fgure 2. We fnd smlar results wth regressons usng the sample, usng the DD-RD specfcaton wth controls and wth hgher order polynomal functons of age. Ths ndcates that after the adopton of Part D, physcans are lkely to ncrease the number of generc drugs prescrbed or contnued durng vsts among patents aged 65 and above relatve to patents under age 65 by 55 percent (calculated as dvded by , the mean of number of generc drugs prescrbed or contnued durng vsts for patents under age 65 before 2006). C. Antcpatory Effect The bottom panel of Table 1 presents regresson results for a sample that ncludes patents vstng physcan offces n We fnd that the magntudes of effects usng the DD- RD specfcaton, wth or wthout covarates, are all greater than those n the top panel. After stratfyng the sample accordng to the year 2006 cutoff and runnng separate regressons on the 21

22 sample before and after 2006, we fnd that the estmated effect for the sample before 2006 s negatve and sgnfcant for the number of drugs prescrbed or contnued. Comparng results n the second column of the bottom panel to those n the top panel, t s apparent that the dfference comes from the decrease n the number of drugs prescrbed or contnued for patents aged 65 and above n It appears that those patents and ther physcans antcpated the upcomng Part D polcy and postponed or reduced the number of drugs prescrbed or contnued untl the tme that Part D coverage took effect. After we nclude the 2005 sample n the regresson, we estmate that Part D results n physcans ncreasng the number of prescrpton drugs prescrbed or contnued for patents aged 65 and above by 45 percent, a more than 28 percent greater effect than comes from our prevous DD-RD estmaton. Ths effect s smlar n sze to the antcpatory effect found n Alpert (2012). There s also evdence that ths antcpatory effect exsts for the number of generc drugs prescrbed or contnued. D. Valdty of Specfcatons and Robustness Checks Ths subsecton descrbes our test of the assumptons of the DD-RD specfcaton, and the senstvty analyses we performed n order to check the robustness of our man results. Frst, we graphcally examned the evdence for sample selecton bas n our specfcaton. Wth the RD desgn, sample selecton bas s a major threat to dentfcaton, because the estmated effect may reflect the change n the composton of the sample, not the effect of the polcy adopton tself. In our context, f the sample before and after the age 65 cut-off was dfferent before and after 2006, then our estmates of the DD-RD specfcaton would be based. In order to valdate the null hypothess of contnuty of the age profle at the age 65 cutoff for the and the samples, Fgure 3 shows scatter plots and ftted cubc age polynomal regresson results. If patents were less lkely to go to ther physcans' offce just before they became elgble for Part D coverage and were more lkely to go after they became elgble then our estmates would suffer from selecton bas. Fgure 3 nstead supports our assumpton and shows no sgn of such bas. There s not much dfference n vst densty at the age 65 cutoff between the and the sample. As a matter of fact, patents have lttle ncentve to change ther frequency of vsts to ther physcans' offce, because patents aged 65 and above are usually elgble for Medcare coverage of physcan vsts, whle patents under age 65 are nfrequently elgble for Medcare coverage of physcan vsts. 22

23 Second, we check whether covarates are dstrbuted smoothly across the age 65 cutoffs for both samples before and after 2006 to further look for selecton bas. We estmate a DD-RD specfed model wth the covarates as outcome varables. Our results are summarzed n Table 2. All but one varable show no dfferental jump at the age 65 cutoffs between the two tme perods. The coeffcent for the race ndcator-- that s, the non-whte dummy -- s only sgnfcant at 10%, and excludng ths varable does not affect our basc results n the man analyses. 29 These results further valdate our assumptons for the DD-RD specfcaton and reduce the possblty of omtted varable bas. Next, we test the robustness of our man results by estmatng the DD-RD model usng dfferent bandwdths, wth and wthout covarates. The results are summarzed n Table 3. We control for dfferent polynomal functons of age n the samples wth dfferent bandwdths. In the frst column, we control for age lnearly wth a bandwdth of one year on each sde of the age 65 cutoff. In the second and thrd columns, we use quadratc and cubc age functons, wth a bandwdth of two years on each sde. The fourth column shows the results wth a cubc age functon and a bandwdth of three years. The ffth and sxth columns show the results wth quartc age functons and bandwdths of fve and sx years on each sde of the age 65 cutoff, respectvely. All of the regresson coeffcents for the number of drugs prescrbed or contnued and the number of generc drugs prescrbed or contnued, wth or wthout covarates, are sgnfcantly postve, wth magntudes smlar to those shown n Table 1. We conclude that adopton of Part D leads to an ncrease n the number of drugs prescrbed or contnued durng physcan vsts for patents begnnng at age 65, especally generc drugs. Fnally, we conduct an extensve set of placebo tests for the man analyss sample (age 60-69), and for samples wth varyng bandwdths, ensure sure that our results represent a causal relatonshp rather than spurous correlaton between Part D and outcomes. We set age 64 as the cutoff and estmate the DD-RD specfcaton, wth and wthout covarates, on all samples. Our results are summarzed n Table 4. The results are statstcally sgnfcant for only three out of 32 specfcatons, and only at the 10 percent levels. Thus the placebo test suggests that our results are not due to spurous correlaton. 29 In results not ncluded n Table 2, we tested whether there was any confoundng ssue for all other physcan and patent characterstcs, ncludng physcan specaltes, patents major dsease categores and seasonal dummes. None of the coeffcents from ths set of regressons were sgnfcant except for one major dsease category: (dseases of the dgestve system). Agan, excludng these covarates does not affect our man results. 23

24 7. Is the Change n Prescrptons n the Best Interest of Patents? Our basc results suggest that physcans do take patents cots nto consderaton and do respond to non-pecunary ncentves by ncreasng the number of drugs prescrbed or contnued durng physcan vsts. In ths secton, we descrbe a seres of supplementary analyses we conduct n order to evaluate other changes n physcan practce patterns n physcan offces and possble mechansms for the documented ncrease n the number of drugs prescrbed or contnued. A. Other Practce Pattern Changes What effect does Part D have on practce patterns n offce settngs, other than prescrpton drug prescrbng? We use the DD-RD specfcaton agan wth cubc and quartc functons of age. Results are summarzed n Table 5; those wth a cubc polynomal of age are n the top panel, and those wth a quartc polynomal of age are n the bottom panel. Frst, we examne the effect of Part D on the number of tests 30 conducted n the physcans' offce (column 1) and patents' tme spent wth ther doctor (column 2). These outcomes may be complementary measures of treatment ntensty durng a doctor s vst. We fnd no sgnfcant effects of Part D on these two outcomes wth ether cubc or quartc polynomals n age as controls. Ths ndcates that physcans do not change treatment patterns for ther patents 65 and above n these other ways when ther patents are more lkely to have drug coverage. These results mply that laboratory tests or patents tme spent wth ther doctor are nether complements nor substtutes for drug treatments (Proposton 2). B. Perfect Agent for Patents or Pharmaceutcal Companes? So far, we have used the ncrease n drugs prescrbed or contnued durng physcan vsts as evdence that physcans act as patents agents. However, f Medcare Part D ncreases pharmaceutcal advertsng (Lakdawalla, Sood, and Gu 2013), then t s possble that the ncrease n drugs prescrbed or contnued comes from the fact that physcans are actng n the nterest of pharmaceutcal companes. We cannot drectly test the mechansm that drves physcans behavors; nevertheless, we provde several peces of evdence suggestng that physcans change 30 We compute the number of tests by countng how many test among those recorded n NAMCS data the patents took. The lst of test recorded n NAMCS s blood pressure check, X-ray, EKG/ECG, Pap test, urnalyss, PSA test, and CBC (complete blood count). 24

25 ther behavor more to do wth the change n costs for patents than wth the behavor of pharmaceutcal companes. Frst, we lnk NAMCS and US FDA Orange Book data to analyze the effect of Part D on the age of actve ngredents of drugs prescrbed or contnued durng physcan vsts. We fnd no sgnfcant results for ths outcome, wth ether cubc or quartc polynomals n age as controls (column 3, Table 5). Ths suggests that physcans dd not prescrbe (or contnue) newer (or older) drugs after the mplementaton of Part D. Nor do we fnd a sgnfcant mpact of Part D on the number of brand-name drugs prescrbed or contnued durng physcan vsts (column 4, Table 5). If physcans act more n the nterests of pharmaceutcal companes than patents, then we would lkely expect an ncrease n the number of new drugs or brand-name drugs prescrbed or contnued durng physcan vsts. However, our results do not support that hypothess. Second, we use the and MEPS to examne the mpact of Part D on patents utlzaton of and expendture on prescrpton drugs, agan usng the DD-RD desgn. Although we don t have specfc date of brth and date of ntervew n the MEPS data, we are stll able to control for a polynomal functon of age n years at the tme of ntervew. Results are reported n Table 6. These results are consstent for all outcomes, controllng for ether a lnear or quadratc functon of age. The number of prescrpton drugs ncreases by a statstcally sgnfcant 8.3 to 11.8 prescrptons per year on average. Ths means that patents ncrease ther use of prescrpton drugs by percent when they turn age 65 after the adopton of Part D as compared to before ts adopton. Total expendtures on prescrpton drugs per ndvdual per year and expendtures on prescrpton drugs from Medcare sources also ncrease at the age 65 cutoff after 2006, although the coeffcents for the former outcome are not precsely estmated. OOP cost and the sgn and magntude of estmated effects are consstent wth those n prevous lterature. 31 However, our effects on these outcomes are not sgnfcant. 32 Also, we fnd that Part 31 Lchtenberg and Sun (2007) found that n 2006, Medcare Part D reduced OOP daly cost of therapy by 18.4 percent and ncreased number of days of therapy by about 12.8 percent compared to that for users under the age of 65. Yn et al. (2008) fnd a 5.9% ncrease n monthly average drug utlzaton and a 13.1% decrease n monthly OOP cost after the penalty free perod (.e., after June 2006) usng those aged as the reference group. Ketcham and Smon (2008) estmate that days supply and number of ndvdual prescrptons flled ncrease by 8.1% and 4.8% respectvely, and OOP costs fall by 17.2% for the always elgble group (over 66 as of 2007) as compared to those who are always nelgble for Medcare (58-64 as of 2007). Engelhardt and Gruber (2011) fnd that Part D has a relatvely small mpact on OOP spendng, and F. X. Lu et al. (2011)fnd that Part D s assocated wth a $ reducton n OOP cost and an ncrease of 2.05 prescrptons per patent year. Kaestner and Khan (2012) found that gettng drug coverage s assocated wth an approxmately 70% ncrease n the use of prescrpton drugs for the general populaton of those aged65 to 85 years old, and a 60% ncrease n the use of prescrpton drugs for those 25

26 D s assocated wth decreases n expendtures on prescrpton drugs from Medcad and prvate sources. Overall, our results suggest that patents dd ncrease drug utlzaton after Part D. Together, these results suggest that the ntroducton of Part D ncreased the number of drugs prescrbed by physcans due to changes n patent OOP costs, and not most or mostly for drugs that are lkely to be most proftable for pharmaceutcal companes. 8. Concluson and Dscusson In ths paper, we examne the mpact of nsurance expanson on a specfc physcan behavor, prescrbng drugs to patents. To explot the consderable change n coverage of prescrpton drugs that resulted from the adopton of Medcare Part D, we compare physcans' prescrbng patterns for those who were elgble for Part D after 2006 to those who were not. Our results are consstent wth physcan consderaton of patents nterests snce we fnd that they ncreased the number of drugs prescrbed or contnued durng physcan vsts. Specfcally, for each patent s vst to a physcan s offce, we estmate a 35% ncrease n the number of drugs prescrbed or contnued and a 55% ncrease n the number of generc drugs prescrbed or contnued followng the mplementaton of Medcare Part D. We use a DD-RD specfcaton to dentfy the effect of Part D on physcan prescrbng behavor, a robust dentfcaton strategy wth the dentfyng assumpton that other determnants of prescrbng patterns are contnuous at the age 65 cutoff before and after 2006,.e., that other determnants of prescrbng patterns do not change precsely at age 65 and precsely n 2006 for reasons other than the ntroducton of Medcare Part D. We also perform an extensve lst of robustness checks to assure that all assumptons of the DD-RD specfcaton are met n our sample. Together those results suggest that physcans take patents costs nto consderaton by ncreasng the number of generc drugs prescrbed or contnued durng physcan vsts. We also study whether physcans antcpated the upcomng drug coverage provded by Part D and thus deferred drug prescrpton untl the tme of ts adopton, a possblty suggested by past lterature. Specfcally, we compare the estmated effect of Part D on prescrpton wth chronc condtons. Bresacher et al. (2011) smulated post-part D outcomes usng tme-seres regressons wth a frst-order autoregressve correlaton structure and pre-part D data, and compared those wth observed results. They concluded that average prescrpton flls per person ncreased sgnfcantly by 1.8 flls n 2006 and 3.4 flls n 2007, compared to a 0.9 fll ncrease per year before Part D. They also found that average OOP drug costs decreased sgnfcantly n both 2006 and 2007, by $143 and $148 per year respectvely. 32 The nsgnfcance of our results for some outcomes n the MEPS could result partly from nsuffcent nformaton n the publc use verson of the MEPS data to precsely estmate age profles and tme trends of outcomes. 26

27 decsons for the sample perod wth and wthout the year Results wth, compared to wthout, 2005 data suggest that gnorng the deferment of some prescrptons n 2005 would result n a 28% overestmaton of the Part D effect. Ths s consstent wth the magntude of the estmated antcpatory effect n Alpert (2012), and provdes addtonal evdence that physcans consder patents' cost when prescrbng drugs. To address the ssue that physcans may be affected by marketng efforts from pharmaceutcal companes, we perform several addtonal analyses. Frst, we lnk the NAMCS and US FDA Orange Book data n order to analyze the effect of Part D on the age of actve ngredents n drugs prescrbed or contnued durng physcan vsts and whether drugs prescrbed or contnued are generc or brand name drugs. If physcans act prmarly n the nterest of pharmaceutcal companes, then they mght change ther practce patterns based on new development of molecules and the advertsng level for branded drugs. However, we fnd that physcans are no more lkely to prescrbe (or contnue) newer drugs after Part D compared to before, nor are they more lkely to ncrease prescrptons for brand-name drugs. We also use the MEPS to examne the mpact of Part D on patents utlzaton of and expendture on prescrpton drugs. Although physcans have the authorty to prescrbe drugs, they do not have the authorty to force patents to comply. Patents complance reflects ther own nterests or preferences; they decde whether to comply or not. If patents ncrease ther drug utlzaton after Part D by nearly as much as the ncrease n the number of prescrptons, t suggests that physcans responses to t are broadly algned wth ther patents nterests as far as they can detect. Indeed, we fnd that utlzaton of prescrpton drugs per person per year rose by 39% after Part D, whch s close to what we observe n the NAMCS data. 33 However, there are caveats to our fndngs. We estmated the average effect of the adopton of Part D. Because around two-thrds of Medcare benefcares had access to drug coverage before 2006, the margnal effect of Part D on those who dd not have prescrpton drug coverage before 2006 could be greater than what we estmated n ths paper. Second, we estmate the ncrease n the propensty of physcans to wrte down the generc rather than the brand name of drugs after compared to before 2006 for those aged compared to those aged 33 The measure for the utlzaton of prescrpton drugs s aggregated to the patent-year level, whch s dfferent from the patent-vst level data that are avalable n NAMCS data for the utlzaton of prescrpton drugs. The dfference between the estmate from NAMCS and MEPS may be due n part to the fact that dfferent drugs requre dfferent day supply amounts and rates of refll. 27

28 The actual number of prescrpton flls for generc drugs s lkely to be hgher than our estmates for both those aged and those aged snce substtuton from brand name to generc equvalents often occurs at the pharmacy. Although ths substtuton may be expected by the physcan, a change n the propensty of physcans to wrte down the generc rather than the brand name of a drug for those aged compared to those aged n 2006 should reflect effects of the ntroducton of Part D unless there was a change n ncentves for those under age 65 that happened at the same tme as Part D was ntroduced though unrelated to Part D. (The ntroducton of Part D could lead to spllover effects causng physcans to change ther prescrbng patterns for patents wth nsurance sources other than Medcare. We do not dstngush n ths paper between man versus spllover effects of the ntroducton of Part D.) Fnally, although we fnd an ncrease n the number of drugs prescrbed or contnued durng physcan vsts for those aged after 2006 relatve to a control group of patents aged 60-64, we do not dstngush whether the ncrease s n new prescrptons wrtten or n ncreased contnuaton or renewal of prescrptons. These ponts may suggest possble drectons for future research. 28

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31 Lundn, Douglas Moral Hazard n Physcan Prescrpton Behavor. Journal of Health Economcs 19 (5): Lure, N, E C Rch, D E Smpson, J Meyer, D L Schedermayer, J L Goodman, and W P McKnney Pharmaceutcal Representatves n Academc Medcal Centers: Interacton wth Faculty and Housestaff. Journal of General Internal Medcne 5 (3): McCrary, Justn Manpulaton of the Runnng Varable n the Regresson Dscontnuty Desgn: A Densty Test. Journal of Econometrcs 142 (2). Elsever: McGure, Thomas G Physcan Agency. Handbook of Health Economcs 1: Meyers, Davd S, Rant Mshor, Jessca McCann, Jose Delgado, Ann S O Malley, and Ed Fryer Prmary Care Physcans Perceptons of the Effect of Insurance Status on Clncal Decson Makng. Annals of Famly Medcne 4 (5): Nguyen, Ha The Prncpal-Agent Problems n Health Care: Evdence from Prescrbng Patterns of Prvate Provders n Vetnam. Health Polcy and Plannng 26 (suppl_1): Pauly, Mark Doctors and Ther Workshops: Economc Models of Physcan Behavor. NBER Books. Natonal Bureau of Economc Research, Inc. Roth, Martn S Patterns n Drect-to-Consumer Prescrpton Durg Prnt Advertsng and Ther Publc Polcy Implcatons. Journal of Publc Polcy & Marketng 15 (1): Sacks, Danel W Physcan Agency, Complance, and Patent Welfare : Evdence from Ant-Cholesterol Drugs. Workng Paper. Stern, Scott, and Manuel Trajtenberg Emprcal Implcatons of Physcan Authorty n Pharmaceutcal Decsonmakng. NBER Workng Paper Seres, no (December). The Henry J. Kaser Famly Foundaton The Medcare Prescrpton Drug Beneft Fact Sheet The Medcare Prescrpton Drug Beneft Fact Sheet a. The Medcare Prescrpton Drug Beneft Fact Sheet b. Medcare Spendng and Fnancng Fact Sheet The Medcare Prescrpton Drug Beneft Fact Sheet. Vakratsas, Demetros, and Tm Ambler How Advertsng Works: What Do We Really Know? Journal of Marketng 63 (1): Wesbrod, Burton A The Health Care Quadrlemma: An Essay on Technologcal Change, Insurance, Qualty of Care, and Cost Contanment. Journal of Economc Lterature 29 (2): Wyna, Matthew K, Jonathan B VanGeest, Deborah S Cummns, and Ira B Wlson Do Physcans Not Offer Useful Servces Because of Coverage Restrctons? Health Affars (Project Hope) 22 (4): Yn, Wesley, Anrban Basu, James X Zhang, Atonu Rabban, Davd O Meltzer, and G Caleb Alexander The Effect of the Medcare Part D Prescrpton Beneft on Drug Utlzaton and Expendtures. Annals of Internal Medcne 148 (3): Yp, Wnne C Physcan Response to Medcare Fee Reductons: Changes n the Volume of Coronary Artery Bypass Graft (CABG) Surgeres n the Medcare and Prvate Sectors. Journal of Health Economcs 17 (6): Zhang, Yutng, Jule M Donohue, Judth R Lave, Gerald O Donnell, and Joseph P Newhouse The Effect of Medcare Part D on Drug and Medcal Spendng. The New England Journal of Medcne 361 (1):

32 Fgure 1: Number of Prescrpton Drugs Prescrbed of Contnued Durng a Physcan Vst, NAMCS and Note: Samples are based on data from the Natonal Ambulatory Medcal Care Survey ( and ). The estmated dscontnutes (and standard errors) at age 65 and the ftted lnes are from a regresson wth a cubc polynomal n age fully nteracted wth a dummy for age greater than or equal to 65 and a dummy for year n or after Ponts represent means for people n each age cell (measured n quarters). 32

33 Fgure 2: Number of Generc Drugs Prescrbed or Contnued Durng a Physcan Vst, NAMCS and Note: Samples are based on data from the Natonal Ambulatory Medcal Care Survey ( and ). The estmated dscontnutes (and standard errors) at age 65 and the ftted lnes are from a regresson wth a cubc polynomal n age fully nteracted wth a dummy for age greater than or equal to 65 and a dummy for year n or after Ponts represent means for people n each age cell (measured n quarters). 33

34 Fgure 3: Dstrbuton of Patent Age at Physcan Vsts, NAMCS and Note: Samples are based on data from the Natonal Ambulatory Medcal Care Survey ( and ). The estmated dscontnutes (and standard errors) at age 65 and the ftted lnes are from a regresson wth a cubc polynomal n age fully nteracted wth a dummy for age greater than or equal to 65 and a dummy for year n or after Ponts represent means for people n each age cell (measured n quarters). 34